Topics for future research and broader issues in SARS-CoV-2 seroprevalence studies merit mention. In this paper Wald-type confidence intervals are considered, which have known limitations [1]}, [2]}; alternative types of confidence intervals could be considered based on the bootstrap [3]}, Bayesian posterior intervals [4]}, or test inversion [5]}. While the approaches here estimate seroprevalence at a fixed point in time, seroprevalence is a dynamic parameter. For analysis of studies with lengthier data collection periods, extensions of the estimators in this paper could be considered which make additional assumptions (e.g., smoothness, monotonicity) about the longitudinal nature of seroprevalence. Another possible extension could consider variations in assay sensitivity, which may depend on a variety of factors such as: the type of assay used; the recency of exposure, infection, or vaccination of an individual; disease severity in infected individuals; the type and dose of vaccine for vaccinated individuals; and so forth. Where additional data are available related to these factors, then extensions of the standardized Rogan-Gladen estimators which incorporate these additional data could be developed. As an alternative to standardization, inverse probability of sampling weights [6]} or inverse odds of sampling weights [7]} could be considered. Standardization and weighting methods may possibly be combined to create a doubly robust Rogan-Gladen estimator.
